Authors:
Sandeep Sharma,Kapil Joshi,Saruchi,Ashish Rayal,Prashant Kumar Choudhary,Anupam Bonkra,Vipin Kumar,Gopal Ghosh,DOI NO:
https://doi.org/10.26782/jmcms.2026.01.00002Keywords:
Artificial Intelligence,Convolutional neural network,Deep Learning,Lumpy skin disease,Lumpy Pixel Ratio,Machine Learning,Precision livestock farming,Abstract
The comprehensive study investigates the application of cutting-edge machine learning algorithms and advanced image processing techniques for the early detection of lumpy skin disease in cattle. The proposed robust analytical framework that evaluates multiple predictive models using comprehensive performance metrics, including F1 scores ranging from 0.87 to 0.97, precision up to 0.984, recall up to 0.963, and accuracy peaking at 97.77%. The novel approach incorporates pixel-level analysis to quantify disease severity through the ratio of affected to healthy tissue, complemented by processing speed delays between 5.54ms and 20.95ms. The research demonstrates significant improvements over traditional diagnostic methods, with particular emphasis on the model's ability to identify high-risk cases requiring immediate intervention. These findings have substantial implications for veterinary medicine, agricultural technology development, and livestock management policies, potentially revolutionizing disease surveillance systems in the agricultural sector.Refference:
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